Abstract for rosti_csl04

Computer Speech and Language, 18(2):181-200, 2004

FACTOR ANALYSED HIDDEN MARKOV MODELS FOR SPEECH RECOGNITION

A-V.I. Rosti & M.J.F. Gales

October 16, 2003

Recently various techniques to improve the correlation model of feature
vector elements in speech recognition systems have been proposed. Such
techniques include semi-tied covariance HMMs and systems based on factor
analysis. All these schemes have been shown to improve the speech
recognition performance without dramatically increasing the number of
model parameters compared to standard diagonal covariance Gaussian
mixture HMMs. This paper introduces a general form of acoustic model,
the factor analysed HMM. A variety of configurations of this model and
parameter sharing schemes, some of which correspond to standard systems,
were examined. An EM algorithm for the parameter optimisation is
presented along with a number of methods to increase the efficiency of
training. The performance of FAHMMs on medium to large vocabulary
continuous speech recognition tasks was investigated. The experiments
show that without elaborate complexity control an equivalent or better
performance compared to a standard diagonal covariance Gaussian mixture
HMM system can be achieved with considerably fewer parameters.

If you have difficulty viewing files that end '.gz',
which are gzip compressed, then you may be able to find
tools to uncompress them at the gzip
web site.

If you have difficulty viewing files that are in PostScript, (ending
'.ps' or '.ps.gz'), then you may be able to
find tools to view them at
the gsview
web site.

We have attempted to provide automatically generated PDF copies of
documents for which only PostScript versions have previously been available.
These are clearly marked in the database - due to the nature of the
automatic conversion process, they are likely to be badly aliased
when viewed at default resolution on screen by acroread.